Embodiments of the present invention relate generally to diagnostic imaging, and more particularly to methods and systems for correlated noise suppression in dual energy imaging.
Non-invasive imaging techniques are widely used for diagnostic imaging in security screening, quality control, and medical imaging systems. Particularly, in medical imaging, a non-invasive imaging technique such as dual-energy imaging is used for unobtrusive, convenient and fast imaging of underlying tissues and organs. Dual energy imaging involves acquisition of projection data at different energy levels within a relatively small time interval. The acquired projection data sets are processed by a decomposition algorithm, which projects the decomposed projection data onto two sets of basis functions. Typically, the basis functions include either the physical components of an X-ray interaction with matter such as photoelectric and Compton scattering, or attenuation coefficients of two materials, such as water and iodine.
Generally, dual energy imaging provides additional and more specific information about an imaged object than single energy techniques such as conventional computed tomography (CT). Dual energy images, however, suffer from a relatively high level of pixel noise due to the decomposition process. Attempts to reduce the pixel noise by increasing exposure leads to increased radiation dosage for clinical use. Accordingly, conventional dual energy imaging techniques extract a noise mask from one of the two decomposed data sets and add the noise mask to the other data set for reducing noise levels. The conventional techniques, however, extract the noise masks for each of the two decomposed data sets individually, thus resulting in blending of artifacts such as image structures into the noise masks.
Accordingly, a recent dual energy imaging technique proposes post-processing the noise masks to suppress the image structures having correlations that differ from the expected behavior of the correlated noise. To that end, the technique employs a smoothing process such as anisotropic diffusion that exploits the correlation information between the two decomposed data sets to detect and suppress image structures in the noise masks. The noise levels in the decomposed data sets, however, are usually much higher than those in the original images, thus making it difficult to separate the noise from the image structures. Accordingly, the anisotropic diffusion process often causes diffusion of the image structures across object edges causing contamination of the noise masks. The contaminated noise masks result in flawed image reconstruction, which in turn, affects the accuracy of a diagnosis.
It is desirable to develop effective methods and systems that enable correlated noise suppression in dual energy imaging. Particularly, there is a need for a technique that enables robust suppression of correlated noise at the object edges to generate high quality images that facilitate a substantially accurate clinical diagnosis.